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Message queues allow services to communicate asynchronously, improving scalability and reliability.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Microservices can communicate using five key patterns based on their needs.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Backpressure helps manage message flow in Kafka during traffic spikes.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

Classifying failures and using controlled retries in Kafka prevents outages.

Backpressure prevents system overload by managing message processing rates in Kafka.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

Request-Response asks for a result, while Events notify that something has happened.

Kafka consumer lag shows how far behind consumers are from new messages.

Kafka consumer offsets help track message processing to avoid loss and duplication.